Сравнение методов

	SHAP (SHapley Additive exPlanations)	Логистическая регрессия	Случайный лес
Область	Машинное обучение	Статистика исследований	Машинное обучение
Семейство	Machine learning	Process / pipeline	Machine learning
Год появления	2017	1958	2001
Автор метода	Lundberg, S.M. & Lee, S.-I.	David Roxbee Cox	Breiman, L.
Тип	Model-explanation method (Shapley-value attribution)	Method	Ensemble (bagging of decision trees)
Основополагающий источник	Lundberg, S.M. & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30, 4766–4777.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Другие названия	SHAP Değerleri (Model Açıklanabilirlik), Shapley additive explanations, SHAP values, model explainability	logit model, binomial logistic regression, LR	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Связанные	5	3	4
Сводка	SHAP is a model-explanation method, introduced by Scott Lundberg and Su-In Lee in 2017, that uses Shapley values from cooperative game theory to measure how much each feature contributes to an individual prediction, making the output of black-box machine-learning models interpretable. It supports both global explanations (overall feature importance) and local explanations (why one specific prediction came out the way it did).	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.